Triplet teaching graph contrastive networks with self-evolving adaptive augmentation

Abstract

Unsupervised graph contrastive learning has recently emerged as the solution to the crisis of label information scarcity for graph data in the real world. However, from the general paradigm of graph contrastive learning, most of the existing methods are still flawed in the design and use of augmented views and the design of contrastive targets. Therefore, the works on how to generate reasonable augmented views and utilize them canonically and how to construct efficient and comprehensive contrastive objectives are very meaningful. Based on the teaching concept, this paper proposes a new triplet teaching graph contrastive network with self-evolving adaptive augmentation. Firstly, after carefully analyzing the internal relationships between different augmented perspectives, we present a triple teaching graph neural network framework based on the improved triplet idea. It creates contrastive objectives depending on different contrastive angle levels, providing thorough guidance for graph encoders. Secondly, a self-evolving adaptive graph augmentation scheme based on topology and feature information is proposed. It is worth mentioning that with the continuous deepening of the training process, the scheme can utilize the learnable self-attention mechanism to constantly supply our network framework with an increasing number of reliable augmented views as input. Finally, when designing the contrastive objectives, we introduce a stochastic hybrid module to mine the unexploited information, which opportunely complements the contrastive sample space formed by our network framework. Furthermore, extensive experiments on multiple real-world node classification datasets demonstrate that our model can generate better-quality node embedding for downstream tasks. The implementation of this paper is available at https://github.com/PaperMiao/T-GCSA.